Comments on: Understanding Natural Language Processing For Social Media Monitoring

By: JasonFalls

JasonFalls — Sat, 05 Dec 2009 01:38:43 +0000

You're welcome. Thanks for the comment!

By: jtorras

jtorras — Fri, 04 Dec 2009 11:56:56 +0000

Thanks for this article Jason!
At Inbenta we are using our “semantic clustering” technology to group conversations that have a similar topic. As you told, they are not necessarily good or bad, but it's interesting to see patterns related to semantic meaning of those conversations

By: Mark A. Chaves (SAS Institute)

Mark A. Chaves (SAS Institute) — Wed, 02 Dec 2009 02:50:59 +0000

Jeff is spot on here… especially with regard to application of NLP within vertical industries… we find that, with our customers, that success is driven by developing “rules” within very specific verticals and the context within those verticals (e.g. ongoing “corporate reputation” issues, known consumer issues/needs, etc.).

Furthermore, by enabling domain experts across customer touch-points (marketing, PR, customer service) to influence how rules get “taught”, accuracy rates can approach the 80% range… any social media measurement / NLP efforts must be seen as a journey…

By: winter123

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By: winter123

winter123 — Thu, 05 Nov 2009 02:32:17 +0000

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By: JasonFalls

JasonFalls — Fri, 30 Oct 2009 17:32:05 +0000

Thanks, Darian. Great to hear from you!

By: JasonFalls

JasonFalls — Fri, 30 Oct 2009 17:32:05 +0000

Thanks, Darian. Great to hear from you!

By: Darian Shimy

Darian Shimy — Thu, 29 Oct 2009 18:30:55 +0000

Jason, great article. I couldn't agree more with the Jeff's comments on accuracy of sentiment and it's usefulness in aggregate. We developed a hybrid sentiment engine that handles poor grammar and typos so I understand first-hand the difficulty in handling social media. Sarcasm is a huge challenge. Thanks!

-- Darian Shimy, VP of Technology Biz360.com

By: Darian Shimy

Darian Shimy — Thu, 29 Oct 2009 18:30:55 +0000

By: anwer

anwer — Tue, 27 Oct 2009 18:59:49 +0000

Great video

By: Eric Melin

Eric Melin — Mon, 26 Oct 2009 13:37:57 +0000

Really great interview. I agree that what makes automated sentiment classification valuable is the ability to uncover trends and make sense of enormous data sets that human vetting just can't accomplish. The rates of accuracy described here ring true; it's still a great overview or “broad brush stroke” tool, as you said.

It becomes vitally important then to make sure you integrate that data into a usable toolset. A virtualization can give you a visual representation that's easier to gain insight from than simple pie charts and graphs.